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CC BY-NC-ND 4.0 · Methods Inf Med 2024; 63(01/02): 011-020
DOI: 10.1055/s-0044-1778694
Original Article

Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets

Marja Fleitmann
1   Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
,
Hristina Uzunova
1   Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
,
René Pallenberg
2   Institute for Signal Processing, University of Lübeck, Schleswig-Holstein, Germany
,
Andreas M. Stroth
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Jan Gerlach
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Alexander Fürschke
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Jörg Barkhausen
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
,
Arpad Bischof
3   Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein (UKSH) Lübeck, Lübeck, Germany
4   IMAGE Information Systems Europe, Rostock, Germany
,
Heinz Handels
1   Artificial Intelligence in Medical Imaging, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
5   Institute of Medical Informatics, University of Lübeck, Schleswig-Holstein, Germany
› Author Affiliations Funding This work was funded by the German Federal Ministry of Education and Research.
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Abstract

Objectives In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature.

Methods This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN). For the selection of optimal parameter subsets all possible combinations of the 22 clinical parameters (age, blood pressure, etc.) are considered using the classification accuracy and precision of the KNN classifier and RDF as quality criteria. Subsequently, the results of the evaluation were optimized by means of feature transformation using regression neural networks (RNN). These were used for a direct classification based on regressed Hounsfield units as well as preprocessing for a subsequent KNN classification.

Results For feature selection, an RDF model achieved the highest accuracy of 84.42% and a KNN model achieved the best precision of 86.21%. The most important parameters include age, height, and hemoglobin. The feature transformation using an RNN considerably exceeded these values with an accuracy of 90.00% and a precision of 97.62% using all 22 parameters as input. However, also the feasibility of the parameter sets in routine clinical practice has to be considered, because some of the 22 parameters are not measured in routine clinical practice and additional measurement time of 15 to 20 minutes per patient is needed. Using the standard feature set available in clinical routine the best accuracy of 86.67% and precision of 93.18% was achieved by the RNN.

Conclusion We developed a reliable hybrid system that helps radiologists determine the optimal contrast dose for CT angiography based on patient-specific parameters.

Keywords

clinical decision support system - contrast medium - CT angiography - deep learning - machine learning


Publication History

Received: 26 April 2022

Accepted: 28 November 2023

Article published online:
23 January 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
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